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Extensive Expertise with Apache NiFi, JAVA and JAVA Spring boot Microservices, AWS, Red Shift, Athena and Big Data technologies. Design, maintain, and lead the development and implementation of robust ETL pipelines using Apache NiFi and Java Spring Boot microservices, to ingest, transform, and integrate large volumes of complex data from multiple sources.
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Databricks Certified Associate Developer for Apache Spark Preferred. Solid experience with big data technologies (Apache Spark, Hadoop, Kafka) and cloud services (AWS, Azure) related to data processing and storage.
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Proficiency in data engineering tools and technologies such as Apache Airflow, Spark, Kafka, Hadoop, or similar. - Implement automated data pipelines using tools like Apache Airflow, Spark, Kafka, or similar technologies, ensuring data is processed efficiently and accurately.
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Expertise in Cloudera Big Data Technologies, including Cloudera Distribution of Hadoop (CDP), Apache Hadoop, Apache Spark, Apache Hive, Apache Impala, and Apache HBase, with hands-on experience in deploying, configuring, and optimizing these technologies for large-scale data processing and analytics.
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Design and implement scalable and reliable data pipelines to ingest, process, and store diverse data at scale, using technologies such as Apache Spark, Hadoop, and Kafka. Utilize ETL tools and frameworks (e.g., Apache Airflow, Talend) to automate data workflows, ensuring efficient data integration and timely availability of data for analytics.
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5+ years of experience developing scalable ETL and ELT workflows using Apache Spark, Apache Airflow, or Apache Flink. 5+ years of experience with Distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka.
$220,000 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Interview Process: 1 internal + 1 client round (possibly 2 client round, no in-person, all rounds will be video) Required Skills: Java, Spring Boot, Rest API, Kafka, Dockers, Kubernetes Nice to have skills: Healthcare experience, Google Cloud, Jenkins Job Summary: A Full Stack Engineer is a versatile role responsible for the development and implementation of both front-end and back-end solutions in a web development environment.
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4+ Experience with technologies Apache Mesos, Marathon, or Apache Spark. 2+ Experience with distributed database systems (Cassandra, ElasticSearch) Utilize programming languages like Java, Python, SQL, Ruby and Go, Container Orchestration services including Docker and Kubernetes, CM tools including Ansible and Terraform, and a variety of AWS tools and services.
$201,400 a yearFull-timeExpandApply NowActive JobUpdated 20 days ago - UpvoteDownvoteShare Job
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Advanced knowledge of Apache Spark, Azure Data Factory, Azure DevOps, Azure Machine Learning, Hadoop, Hive (Apache), Informatica, Microsoft Azure, Databricks, Microsoft Power BI, Microsoft SharePoint, Microsoft SQL Server, MS Office Products or Operational Data Integration (OLTP.
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Frameworks: Utilize Apache Struts, Spring Boot, and implement design patterns such as DAO, Singleton, and Service Locator. Frameworks & Tools: Extensive experience with Hibernate/JPA, AWS, Databricks, Angular/React JS, and JBOSS Enterprise SOA. Knowledge of Apache Kafka, Apache Spark, and ActiveMQ is a plus.
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Concepts Java, multithreading et programmation orientée objet, développement d'API RESTful à l'aide de Spring Boot, architecture basée sur des microservices, intégration de bases de données, mise en œuvre d'une communication basée sur les messages à l'aide de technologies comme Apache Kafka pour le traitement asynchrone, GIT GitHub/BitBucket.
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You will leverage your expertise in Databricks, Apache Spark, and Azure to design, develop, and implement data warehouses, data lakehouses, and AI/ML models that fuel our data-driven operations.
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Key Words: Java | Angular | TypeScript | AWS Cloud | SQL | Big Data | Spring Boot | Databricks | Cassandra | Kubernetes | Microservices | Spark | Databricks |. Familiarity with cloud technology stacks including AWS, Kubernetes, Spring Boot, Kafka, SSO, and Cassandra.
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Responsible for utilizing big data technologies (e.g., Apache Hive, Apache, Pig, Apache Spark, MapReduce, Hadoop, and MongoDB) and knowledge of data science models to analyze large amounts of data and create valuable insights for supporting the mission.
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Frameworks: Spring Framework, Spring Boot, Kafka, AMQP.Front-end Tools: Thymeleaf, NodeJS.Build/Deployment Tools: Gradle, Terraform, Helm, Git. DevOps Toolchain: SonarQube, Slack, GitHub, ElasticSearch, Logstash, Kibana, Fluentbit, Apache NiFi. Other Skills: Linux/Unix shell scripting, object-oriented design principles, proficiency in Microsoft Office Tools, Agile methodologies (SCRUM), and DevOps practices (Continuous Integration and Continuous Deployment.
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